2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)最新文献_第8页

Is Software Engineering Research Addressing Software Engineering Problems? (Keynote) 软件工程研究解决了软件工程问题吗?(主题)

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3417103

G. Murphy

引用次数: 1

Identifying Mutation Subsumption Relations 识别突变包容关系

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418921

Beatriz Souza

引用次数: 4

Towards Transparency-Encouraging Partial Software Disclosure to Enable Trust in Data Usage 迈向透明——鼓励部分软件公开，以实现对数据使用的信任

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3415282

Christopher M. Schindler

引用次数: 1

Cats Are Not Fish: Deep Learning Testing Calls for Out-Of-Distribution Awareness 猫不是鱼:深度学习测试需要超出分布的意识

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416609

David Berend, Xiaofei Xie, L. Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao

{"title":"Cats Are Not Fish: Deep Learning Testing Calls for Out-Of-Distribution Awareness","authors":"David Berend, Xiaofei Xie, L. Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao","doi":"10.1145/3324884.3416609","DOIUrl":"https://doi.org/10.1145/3324884.3416609","url":null,"abstract":"As Deep Learning (DL) is continuously adopted in many industrial applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. According to the fundamental assumption of deep learning, the DL software does not provide statistical guarantee and has limited capability in handling data that falls outside of its learned distribution, i.e., out-of-distribution (OOD) data. Although recent progress has been made in designing novel testing techniques for DL software, which can detect thousands of errors, the current state-of-the-art DL testing techniques usually do not take the distribution of generated test data into consideration. It is therefore hard to judge whether the “identified errors” are indeed meaningful errors to the DL application (i.e., due to quality issues of the model) or outliers that cannot be handled by the current model (i.e., due to the lack of training data). Tofill this gap, we take thefi rst step and conduct a large scale empirical study, with a total of 451 experiment configurations, 42 deep neural networks (DNNs) and 1.2 million test data instances, to investigate and characterize the impact of OOD-awareness on DL testing. We further analyze the consequences when DL systems go into production by evaluating the effectiveness of adversarial retraining with distribution-aware errors. The results confirm that introducing data distribution awareness in both testing and enhancement phases outperforms distribution unaware retraining by up to 21.5%.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52

Scaling Client-Specific Equivalence Checking via Impact Boundary Search 通过影响边界搜索扩展客户端特定的等价性检查

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416634

Nick Feng, Federico Mora, V. Hui, M. Chechik

引用次数: 3

ER Catcher: A Static Analysis Framework for Accurate and Scalable Event-Race Detection in Android ER捕手:一个用于Android中精确和可扩展的事件竞争检测的静态分析框架

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416639

Navid Salehnamadi, Abdulaziz Alshayban, Iftekhar Ahmed, S. Malek

引用次数: 4

Lightweight MBT Testing for National e-Health Portal in Norway 挪威国家电子卫生门户网站轻量级MBT测试

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3421843

D. Gafurov, Margrete Sunde Grovan, Arne Erik Hurum

{"title":"Lightweight MBT Testing for National e-Health Portal in Norway","authors":"D. Gafurov, Margrete Sunde Grovan, Arne Erik Hurum","doi":"10.1145/3324884.3421843","DOIUrl":"https://doi.org/10.1145/3324884.3421843","url":null,"abstract":"We present lightweight model-based testing (MBT) of privacy and authorization concepts of national portal for electronic health services in Norway (which has over a million of visits per month). We have developed test models for creating and updating privacy levels and authorization categories using finite state machine. Our models emphasize not only positive but also negative behavioral aspects of the system. Using edge and edge-pair coverage as an acceptance criteria we identify and systematically derive abstract tests (high level user scenario) from models. Abstract tests are further refined and transformed into concrete tests with detailed steps and data. Although derivation of abstract tests and their transformation into concrete ones are manual, execution of concrete tests and generation of test report are automated. In total, we extracted 85 abstract test cases which resulted in 80 concrete test cases with over 550 iterations. Automated execution of all tests takes about 1 hour, while manual execution of one takes about 5 minutes (over 40 times speedup). MBT contributed to shift the focus of our intellectual work effort into model design rather than test case design, thus making derivation of test scenarios systematic and straight forward. In addition, applying MBT augmented and extended our traditional quality assurance techniques by facilitating better comprehension of new privacy and authorization concepts. Graphical models served as a useful aid in learning these concepts for newcomers.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117172372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

The Classification and Propagation of Program Comments 程序注释的分类与传播

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418913

Xiangzhe Xu

{"title":"The Classification and Propagation of Program Comments","authors":"Xiangzhe Xu","doi":"10.1145/3324884.3418913","DOIUrl":"https://doi.org/10.1145/3324884.3418913","url":null,"abstract":"Natural language comments are like bridges between human logic and software semantics. Developers use comments to describe the function, implementation, and property of code snippets. This kind of connections contains rich information, like the potential types of a variable and the pre-condition of a method, among other things. In this paper, we categorize comments and use natural language processing techniques to extract information from them. Based on the semantics of programming languages, different rules are built for each comment category to systematically propagate comments among code entities. Then we use the propagated comments to check the code usage and comments consistency. Our demo system finds 37 bugs in real-world projects, 30 of which have been confirmed by the developers. Except for bugs in the code, we also find 304 pieces of defected comments. The 12 of them are misleading and 292 of them are not correct. Moreover, among the 41573 pieces of comments we propagate, 87 comments are for private native methods which had neither code nor comments. We also conduct a user study where we find that propagated comments are as good as human-written comments in three dimensions of consistency, naturalness, and meaningfulness.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129047194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Robust Production Machine Learning Systems: Managing Dataset Shift 迈向稳健的生产机器学习系统:管理数据集转移

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3415281

Hala Abdelkader

引用次数: 7

A Framework for Automated Test Mocking of Mobile Apps 移动应用程序的自动化测试模拟框架

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418927

M. Fazzini, Alessandra Gorla, A. Orso

{"title":"A Framework for Automated Test Mocking of Mobile Apps","authors":"M. Fazzini, Alessandra Gorla, A. Orso","doi":"10.1145/3324884.3418927","DOIUrl":"https://doi.org/10.1145/3324884.3418927","url":null,"abstract":"Mobile apps interact with their environment extensively, and these interactions can complicate testing activities because test cases may need a complete environment to be executed. Interactions with the environment can also introduce test flakiness, for instance when the environment behaves in non-deterministic ways. For these reasons, it is common to create test mocks that can eliminate the need for (part of) the environment to be present during testing. Manual mock creation, however, can be extremely time consuming and error-prone. Moreover, the generated mocks can typically only be used in the context of the specific tests for which they were created. To address these issues, we propose MOKA, a general framework for collecting and generating reusable test mocks in an automated way. MOKA leverages the ability to observe a large number of interactions between an application and its environment and uses an iterative approach to generate two possible, alternative types of mocks with different reusability characteristics: advanced mocks generated through program synthesis (ideally) and basic record-replay-based mocks (as a fallback solution). In this paper, we describe the new ideas behind MOKA, its main characteristics, a preliminary empirical study, and a set of possible applications that could benefit from our framework.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121623730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6